A review of safe reinforcement learning: Methods, theory and applications
Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …
Machine learning of spatial data
B Nikparvar, JC Thill - ISPRS International Journal of Geo-Information, 2021 - mdpi.com
Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …
learning for spatial domains of application. At the same time, resources that would identify …
Rank2tell: A multimodal driving dataset for joint importance ranking and reasoning
E Sachdeva, N Agarwal, S Chundi… - Proceedings of the …, 2024 - openaccess.thecvf.com
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver
assistance systems (ADAS) may largely depend on their acceptance by society, for which …
assistance systems (ADAS) may largely depend on their acceptance by society, for which …
Spatio-temporal graph dual-attention network for multi-agent prediction and tracking
An effective understanding of the environment and accurate trajectory prediction of
surrounding dynamic obstacles are indispensable for intelligent mobile systems (eg …
surrounding dynamic obstacles are indispensable for intelligent mobile systems (eg …
Interaction-aware decision-making for automated vehicles using social value orientation
Motion control algorithms in the presence of pedestrians are critical for the development of
safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on …
safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on …
Spectral temporal graph neural network for trajectory prediction
An effective understanding of the contextual environment and accurate motion forecasting of
surrounding agents is crucial for the development of autonomous vehicles and social mobile …
surrounding agents is crucial for the development of autonomous vehicles and social mobile …
Rain: Reinforced hybrid attention inference network for motion forecasting
Motion forecasting plays a significant role in various domains (eg, autonomous driving,
human-robot interaction), which aims to predict future motion sequences given a set of …
human-robot interaction), which aims to predict future motion sequences given a set of …
Safe multi-agent reinforcement learning for multi-robot control
A challenging problem in robotics is how to control multiple robots cooperatively and safely
in real-world applications. Yet, developing multi-robot control methods from the perspective …
in real-world applications. Yet, developing multi-robot control methods from the perspective …
Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach
This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
Autonomous driving strategies at intersections: Scenarios, state-of-the-art, and future outlooks
Due to the complex and dynamic character of intersection scenarios, the autonomous
driving strategy at intersections has been a difficult problem and a hot point in the research …
driving strategy at intersections has been a difficult problem and a hot point in the research …